The present application relates generally to performing promotion management functions in a marketing system, and more particularly to techniques for generating offers to a consumer with a view towards influencing the consumer's purchasing behavior.
Several techniques are used presently to influence the purchasing behavior of consumers. Typically, these techniques offer discounts or other incentives to consumers on goods and services which are to be promoted. For example, printed coupons offering discounts on the promoted products may be distributed to consumers and may be redeemed by the consumers when a consumer purchases the promoted product at the point-of-sale (POS). These coupons are generally distributed to consumers in a random manner or in a more demographically focused manner, e.g. via blanket mailings to residents of a neighborhood or region. A major drawback of this method of distribution is that the coupons are not targeted to consumers most likely to use the coupons. This often results in consumers receiving coupons which are irrelevant and uninteresting to the consumers. As a result, a large percentage of the coupons are never redeemed, and are hence very ineffective in influencing a consumer's purchasing behavior. Further, from the manufacturer's and retailer's, or any seller's standpoint, the resources and money needed to print and distribute the coupons is not efficiently used and subsequently wasted.
With the advent of loyalty cards which enable point-of-sale systems to capture consumer purchase history information which comprises information related to purchases made by the consumer. By basing the distribution of coupons upon the consumer purchase history information, retailers and manufacturers have had better success in targeting potential purchasers of a particular product. Additionally, the distribution may be based upon demographic information provided by the consumer when applying for the loyalty card. Retailers and/or manufacturers are now able to target potential purchasers for a product by executing simple queries (e.g. “People who buy Product X more than twice per week and who spend more than $30 per month at a store”) against the captured information. Consumers who match the query criteria then receive an incentive offer or coupon on a product associated with the query criteria. According to another technique, based on the captured information, the consumers are classified into one or more segments based on geodemographic characteristics, economic characteristics, age characteristics, etc. associated with the consumers. Incentives or coupons are then distributed to consumers based on the segment(s) to which the consumer belongs.
While the “query-based” and “segment-based” techniques described above achieve better results than a random or blanket targeting technique, they fail to take into consideration a particular consumer's unique shopping preferences which are not truly represented by either the segment in which the consumer is classified or by the query criteria. Further, a majority of above-mentioned techniques fail to take into consideration the consumer's response to the incentives or offers. For example, information such as information relating to offers which are not responded to by a consumer, which may be vital for making a subsequent offer to the consumer, is not captured or even considered by conventional techniques. As a result, conventional marketing techniques fail to achieve the one-to-one (1-1) target marketing desired by retailers and manufacturers. Effective 1-1 targeting of consumers thus remains difficult and elusive.
In light of the above, there is a need for marketing techniques which achieve one-to-one marketing and which resolve disadvantages and limitations of conventional systems. It is also desirable that these marketing techniques be easily scalable. It is also desirable that these techniques allow both retailers and manufacturers to maximize profits and foster brand loyalty while allowing them to reward their loyal consumers.